from sklearn_benchmarks.report import Reporting, ReportingHpo
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 35.0 | 17.006379 |
| daal4py_KNeighborsClassifier | 0.0 | 2.0 | 40.781562 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 52.845766 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 32.120255 |
| KMeans_tall | 0.0 | 0.0 | 23.598090 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 9.046897 |
| KMeans_short | 0.0 | 0.0 | 2.868130 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.450224 |
| LogisticRegression | 0.0 | 0.0 | 20.986228 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 4.508078 |
| Ridge | 0.0 | 0.0 | 11.559054 |
| daal4py_Ridge | 0.0 | 0.0 | 2.074090 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 3.028100 |
| lightgbm | 0.0 | 5.0 | 55.957548 |
| xgboost | 0.0 | 5.0 | 18.885932 |
| catboost | 0.0 | 5.0 | 3.584815 |
| total | 1.0 | 4.0 | 0.390695 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.180 | 0.000 | 4.435 | 0.000 | 1 | 1 | NaN | NaN | 0.515 | 0.000 | 0.350 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.838 | 0.102 | 0.000 | 0.014 | 1 | 1 | 0.735 | 0.928 | 1.956 | 0.086 | 7.076 | 0.314 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.218 | 0.011 | 0.000 | 0.218 | 1 | 1 | 1.000 | 1.000 | 0.098 | 0.001 | 2.213 | 0.116 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.198 | 0.000 | 4.040 | 0.000 | 1 | 100 | NaN | NaN | 0.501 | 0.000 | 0.395 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.063 | 0.102 | 0.000 | 0.023 | 1 | 100 | 0.937 | 0.834 | 1.868 | 0.026 | 12.346 | 0.179 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.223 | 0.001 | 0.000 | 0.223 | 1 | 100 | 1.000 | 1.000 | 0.099 | 0.002 | 2.256 | 0.048 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.145 | 0.000 | 5.515 | 0.000 | -1 | 1 | NaN | NaN | 0.501 | 0.000 | 0.289 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 26.618 | 0.288 | 0.000 | 0.027 | -1 | 1 | 0.735 | 0.724 | 1.884 | 0.025 | 14.129 | 0.241 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.192 | 0.018 | 0.000 | 0.192 | -1 | 1 | 1.000 | 1.000 | 0.100 | 0.002 | 1.923 | 0.189 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.145 | 0.000 | 5.524 | 0.000 | -1 | 100 | NaN | NaN | 0.506 | 0.000 | 0.286 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 35.714 | 0.000 | 0.000 | 0.036 | -1 | 100 | 0.937 | 0.928 | 1.959 | 0.024 | 18.232 | 0.225 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.199 | 0.012 | 0.000 | 0.199 | -1 | 100 | 1.000 | 1.000 | 0.101 | 0.001 | 1.972 | 0.124 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.145 | 0.000 | 5.510 | 0.000 | -1 | 5 | NaN | NaN | 0.505 | 0.000 | 0.287 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 35.762 | 0.000 | 0.000 | 0.036 | -1 | 5 | 0.843 | 0.724 | 1.908 | 0.019 | 18.743 | 0.183 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.202 | 0.017 | 0.000 | 0.202 | -1 | 5 | 1.000 | 1.000 | 0.101 | 0.002 | 1.996 | 0.171 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.138 | 0.000 | 5.805 | 0.000 | 1 | 5 | NaN | NaN | 0.509 | 0.000 | 0.271 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.082 | 0.173 | 0.000 | 0.023 | 1 | 5 | 0.843 | 0.834 | 1.930 | 0.014 | 11.958 | 0.123 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.220 | 0.002 | 0.000 | 0.220 | 1 | 5 | 1.000 | 1.000 | 0.103 | 0.007 | 2.141 | 0.156 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.287 | 0.000 | 1 | 1 | NaN | NaN | 0.102 | 0.000 | 0.544 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.821 | 0.032 | 0.000 | 0.011 | 1 | 1 | 0.975 | 0.989 | 0.343 | 0.005 | 31.530 | 0.489 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.006 | 0.000 | 2.351 | 0.156 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.283 | 0.000 | 1 | 100 | NaN | NaN | 0.102 | 0.000 | 0.554 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.389 | 0.086 | 0.000 | 0.021 | 1 | 100 | 0.987 | 0.989 | 0.283 | 0.005 | 75.623 | 1.393 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.028 | 0.001 | 0.000 | 0.028 | 1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 4.713 | 0.253 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.277 | 0.000 | -1 | 1 | NaN | NaN | 0.102 | 0.000 | 0.568 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.401 | 0.199 | 0.000 | 0.023 | -1 | 1 | 0.975 | 0.979 | 0.286 | 0.006 | 81.910 | 1.809 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.001 | 0.000 | 0.020 | -1 | 1 | 1.000 | 1.000 | 0.006 | 0.000 | 3.282 | 0.244 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.275 | 0.000 | -1 | 100 | NaN | NaN | 0.102 | 0.000 | 0.572 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.809 | 0.000 | 0.000 | 0.034 | -1 | 100 | 0.987 | 0.989 | 0.341 | 0.007 | 99.160 | 2.030 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.034 | 0.002 | 0.000 | 0.034 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 5.674 | 0.381 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.277 | 0.000 | -1 | 5 | NaN | NaN | 0.102 | 0.000 | 0.563 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.899 | 0.000 | 0.000 | 0.034 | -1 | 5 | 0.982 | 0.979 | 0.288 | 0.006 | 117.569 | 2.473 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.034 | 0.002 | 0.000 | 0.034 | -1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 5.502 | 0.429 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.281 | 0.000 | 1 | 5 | NaN | NaN | 0.103 | 0.000 | 0.550 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.470 | 0.028 | 0.000 | 0.021 | 1 | 5 | 0.982 | 0.989 | 0.291 | 0.003 | 73.752 | 0.886 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.001 | 0.000 | 0.030 | 1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 4.678 | 0.225 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.536 | 0.000 | 0.023 | 0.000 | 1 | 100 | NaN | NaN | 0.767 | 0.000 | 4.611 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 5.199 | 0.057 | 0.000 | 0.005 | 1 | 100 | 0.981 | 0.962 | 0.127 | 0.004 | 41.018 | 1.273 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.008 | 0.001 | 0.000 | 0.008 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 39.552 | 23.991 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.516 | 0.000 | 0.023 | 0.000 | 1 | 5 | NaN | NaN | 0.778 | 0.000 | 4.519 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.554 | 0.015 | 0.000 | 0.002 | 1 | 5 | 0.980 | 0.962 | 0.127 | 0.001 | 12.224 | 0.175 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 9.810 | 6.157 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.571 | 0.000 | 0.022 | 0.000 | -1 | 1 | NaN | NaN | 0.748 | 0.000 | 4.773 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.486 | 0.004 | 0.000 | 0.000 | -1 | 1 | 0.965 | 0.973 | 0.659 | 0.008 | 0.738 | 0.011 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 2.796 | 1.214 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.491 | 0.000 | 0.023 | 0.000 | -1 | 5 | NaN | NaN | 0.760 | 0.000 | 4.594 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.920 | 0.005 | 0.000 | 0.001 | -1 | 5 | 0.980 | 0.973 | 0.228 | 0.005 | 4.033 | 0.088 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 12.845 | 6.952 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.383 | 0.000 | 0.024 | 0.000 | -1 | 100 | NaN | NaN | 0.740 | 0.000 | 4.572 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.981 | 0.043 | 0.000 | 0.003 | -1 | 100 | 0.981 | 0.973 | 0.229 | 0.002 | 13.030 | 0.225 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.010 | 0.000 | 0.000 | 0.010 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 29.711 | 13.466 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.507 | 0.000 | 0.023 | 0.000 | 1 | 1 | NaN | NaN | 0.752 | 0.000 | 4.665 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.807 | 0.002 | 0.000 | 0.001 | 1 | 1 | 0.965 | 0.973 | 0.660 | 0.018 | 1.222 | 0.033 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 1.222 | 0.653 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.846 | 0.000 | 0.019 | 0.000 | 1 | 100 | NaN | NaN | 0.467 | 0.000 | 1.812 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.057 | 0.001 | 0.000 | 0.000 | 1 | 100 | 0.983 | 0.970 | 0.001 | 0.000 | 74.849 | 31.025 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 6.049 | 5.305 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.863 | 0.000 | 0.019 | 0.000 | 1 | 5 | NaN | NaN | 0.475 | 0.000 | 1.816 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.979 | 0.970 | 0.001 | 0.000 | 36.703 | 14.334 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.618 | 5.061 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.823 | 0.000 | 0.019 | 0.000 | -1 | 1 | NaN | NaN | 0.480 | 0.000 | 1.715 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.971 | 0.983 | 0.007 | 0.000 | 3.823 | 0.233 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 18.519 | 15.750 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.822 | 0.000 | 0.019 | 0.000 | -1 | 5 | NaN | NaN | 0.467 | 0.000 | 1.759 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.027 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.979 | 0.983 | 0.001 | 0.000 | 24.711 | 7.452 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 18.977 | 16.038 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.826 | 0.000 | 0.019 | 0.000 | -1 | 100 | NaN | NaN | 0.469 | 0.000 | 1.760 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.047 | 0.000 | 0.000 | 0.000 | -1 | 100 | 0.983 | 0.983 | 0.001 | 0.000 | 46.019 | 13.653 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 20.622 | 17.515 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.860 | 0.000 | 0.019 | 0.000 | 1 | 1 | NaN | NaN | 0.474 | 0.000 | 1.814 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.971 | 0.983 | 0.007 | 0.001 | 3.347 | 0.353 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.809 | 3.972 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.635 | 0.000 | 0.755 | 0.000 | k-means++ | NaN | 30 | NaN | 0.456 | 0.0 | 1.394 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.369 | 0.000 | k-means++ | 0.001 | 30 | 0.000 | 0.000 | 0.0 | 8.300 | 5.549 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.411 | 8.845 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.503 | 0.000 | 0.954 | 0.000 | random | NaN | 30 | NaN | 0.407 | 0.0 | 1.238 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.374 | 0.000 | random | 0.000 | 30 | 0.001 | 0.000 | 0.0 | 8.314 | 5.371 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.055 | 7.622 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.846 | 0.000 | 3.506 | 0.000 | k-means++ | NaN | 30 | NaN | 3.304 | 0.0 | 2.072 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 14.092 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.751 | 2.897 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.001 | 0.015 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.663 | 9.414 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.358 | 0.000 | 3.775 | 0.000 | random | NaN | 30 | NaN | 3.159 | 0.0 | 2.013 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 15.167 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.446 | 3.064 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.019 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.914 | 8.241 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.234 | 0.0 | 0.014 | 0.000 | k-means++ | NaN | 20 | NaN | 0.089 | 0.0 | 2.634 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.185 | 0.000 | k-means++ | -0.001 | 20 | 0.000 | 0.001 | 0.0 | 2.631 | 0.718 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.663 | 7.795 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.079 | 0.0 | 0.040 | 0.000 | random | NaN | 20 | NaN | 0.032 | 0.0 | 2.496 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.179 | 0.000 | random | -0.000 | 20 | -0.001 | 0.001 | 0.0 | 2.559 | 0.522 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.374 | 6.751 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.630 | 0.0 | 0.254 | 0.000 | k-means++ | NaN | 20 | NaN | 0.350 | 0.0 | 1.798 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.530 | 0.000 | k-means++ | 0.269 | 20 | 0.295 | 0.001 | 0.0 | 2.066 | 0.391 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.555 | 5.057 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.224 | 0.0 | 0.715 | 0.000 | random | NaN | 20 | NaN | 0.143 | 0.0 | 1.567 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 6.348 | 0.000 | random | 0.320 | 20 | 0.307 | 0.001 | 0.0 | 2.064 | 0.383 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 6.915 | 4.436 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.722 | 0.0 | [-0.10065769] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.232 | 0.0 | 5.253 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [55.64009759] | 0.000 | NaN | NaN | NaN | NaN | 0.551 | 0.000 | 0.0 | 0.755 | 0.438 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.23791645] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.398 | 0.406 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.859 | 0.0 | [2.42170759] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.819 | 0.0 | 1.049 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [116.3626664] | 0.000 | NaN | NaN | NaN | NaN | 0.300 | 0.003 | 0.0 | 0.573 | 0.125 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [20.36205296] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.001 | 0.0 | 0.151 | 0.113 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.203 | 0.0 | 0.394 | 0.0 | NaN | NaN | NaN | 0.195 | 0.0 | 1.040 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.013 | 0.0 | 6.144 | 0.0 | NaN | NaN | 0.1 | 0.021 | 0.0 | 0.606 | 0.016 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.082 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.669 | 0.707 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.425 | 0.0 | 0.562 | 0.0 | NaN | NaN | NaN | 0.259 | 0.0 | 5.490 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 5.077 | 0.0 | NaN | NaN | 1.0 | 0.000 | 0.0 | 0.585 | 0.477 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.013 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.656 | 0.759 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()